Class TextEmbeddingModel (1.95.1)
Stay organized with collections
Save and categorize content based on your preferences.
TextEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)
Creates a LanguageModel.
This constructor should not be called directly.
Use LanguageModel.from_pretrained(model_name=...)
instead.
Methods
batch_predict
batch_predict(
*,
dataset: typing.Union[str, typing.List[str]],
destination_uri_prefix: str,
model_parameters: typing.Optional[typing.Dict] = None
) -> google.cloud.aiplatform.jobs.BatchPredictionJob
Starts a batch prediction job with the model.
Exceptions |
Type |
Description |
ValueError |
When source or destination URI is not supported. |
count_tokens
count_tokens(
prompts: typing.List[str],
) -> vertexai.preview.language_models.CountTokensResponse
Counts the tokens and billable characters for a given prompt.
Note: this does not make a prediction request to the model, it only counts the tokens
in the request.
Parameter |
Name |
Description |
prompts |
List[str]
Required. A list of prompts to ask the model. For example: ["What should I do today?", "How's it going?"]
|
deploy_tuned_model
deploy_tuned_model(
tuned_model_name: str,
machine_type: typing.Optional[str] = None,
accelerator: typing.Optional[str] = None,
accelerator_count: typing.Optional[int] = None,
) -> vertexai.language_models._language_models._LanguageModel
Loads the specified tuned language model.
from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T
Loads a _ModelGardenModel.
Exceptions |
Type |
Description |
ValueError |
If model_name is unknown. |
ValueError |
If model does not support this class. |
get_embeddings
get_embeddings(
texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],
*,
auto_truncate: bool = True,
output_dimensionality: typing.Optional[int] = None
) -> typing.List[vertexai.language_models.TextEmbedding]
Calculates embeddings for the given texts.
get_embeddings_async
get_embeddings_async(
texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],
*,
auto_truncate: bool = True,
output_dimensionality: typing.Optional[int] = None
) -> typing.List[vertexai.language_models.TextEmbedding]
Asynchronously calculates embeddings for the given texts.
get_tuned_model
get_tuned_model(*args, **kwargs)
Loads the specified tuned language model.
list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]
Lists the names of tuned models.
tune_model
tune_model(
*,
training_data: typing.Optional[str] = None,
corpus_data: typing.Optional[str] = None,
queries_data: typing.Optional[str] = None,
test_data: typing.Optional[str] = None,
validation_data: typing.Optional[str] = None,
batch_size: typing.Optional[int] = None,
train_steps: typing.Optional[int] = None,
tuned_model_location: typing.Optional[str] = None,
model_display_name: typing.Optional[str] = None,
task_type: typing.Optional[str] = None,
machine_type: typing.Optional[str] = None,
accelerator: typing.Optional[str] = None,
accelerator_count: typing.Optional[int] = None,
output_dimensionality: typing.Optional[int] = None,
learning_rate_multiplier: typing.Optional[float] = None
) -> vertexai.language_models._language_models._TextEmbeddingModelTuningJob
Tunes a model based on training data.
This method launches and returns an asynchronous model tuning job.
Usage:
tuning_job = model.tune_model(...)
... do some other work
tuned_model = tuning_job.deploy_tuned_model() # Blocks until tuning is complete
Exceptions |
Type |
Description |
ValueError |
If the provided parameter combinations or values are not supported. |
RuntimeError |
If the model does not support tuning |
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[],[],null,["# Class TextEmbeddingModel (1.95.1)\n\nVersion latestkeyboard_arrow_down\n\n- [1.95.1 (latest)](/python/docs/reference/vertexai/latest/vertexai.language_models.TextEmbeddingModel)\n- [1.94.0](/python/docs/reference/vertexai/1.94.0/vertexai.language_models.TextEmbeddingModel)\n- [1.93.1](/python/docs/reference/vertexai/1.93.1/vertexai.language_models.TextEmbeddingModel)\n- [1.92.0](/python/docs/reference/vertexai/1.92.0/vertexai.language_models.TextEmbeddingModel)\n- [1.91.0](/python/docs/reference/vertexai/1.91.0/vertexai.language_models.TextEmbeddingModel)\n- [1.90.0](/python/docs/reference/vertexai/1.90.0/vertexai.language_models.TextEmbeddingModel)\n- [1.89.0](/python/docs/reference/vertexai/1.89.0/vertexai.language_models.TextEmbeddingModel)\n- [1.88.0](/python/docs/reference/vertexai/1.88.0/vertexai.language_models.TextEmbeddingModel)\n- [1.87.0](/python/docs/reference/vertexai/1.87.0/vertexai.language_models.TextEmbeddingModel)\n- [1.86.0](/python/docs/reference/vertexai/1.86.0/vertexai.language_models.TextEmbeddingModel)\n- [1.85.0](/python/docs/reference/vertexai/1.85.0/vertexai.language_models.TextEmbeddingModel)\n- [1.84.0](/python/docs/reference/vertexai/1.84.0/vertexai.language_models.TextEmbeddingModel)\n- [1.83.0](/python/docs/reference/vertexai/1.83.0/vertexai.language_models.TextEmbeddingModel)\n- [1.82.0](/python/docs/reference/vertexai/1.82.0/vertexai.language_models.TextEmbeddingModel)\n- [1.81.0](/python/docs/reference/vertexai/1.81.0/vertexai.language_models.TextEmbeddingModel)\n- [1.80.0](/python/docs/reference/vertexai/1.80.0/vertexai.language_models.TextEmbeddingModel)\n- [1.79.0](/python/docs/reference/vertexai/1.79.0/vertexai.language_models.TextEmbeddingModel)\n- [1.78.0](/python/docs/reference/vertexai/1.78.0/vertexai.language_models.TextEmbeddingModel)\n- [1.77.0](/python/docs/reference/vertexai/1.77.0/vertexai.language_models.TextEmbeddingModel)\n- [1.76.0](/python/docs/reference/vertexai/1.76.0/vertexai.language_models.TextEmbeddingModel)\n- [1.75.0](/python/docs/reference/vertexai/1.75.0/vertexai.language_models.TextEmbeddingModel)\n- [1.74.0](/python/docs/reference/vertexai/1.74.0/vertexai.language_models.TextEmbeddingModel)\n- [1.73.0](/python/docs/reference/vertexai/1.73.0/vertexai.language_models.TextEmbeddingModel)\n- [1.72.0](/python/docs/reference/vertexai/1.72.0/vertexai.language_models.TextEmbeddingModel)\n- [1.71.1](/python/docs/reference/vertexai/1.71.1/vertexai.language_models.TextEmbeddingModel)\n- [1.70.0](/python/docs/reference/vertexai/1.70.0/vertexai.language_models.TextEmbeddingModel)\n- [1.69.0](/python/docs/reference/vertexai/1.69.0/vertexai.language_models.TextEmbeddingModel)\n- [1.68.0](/python/docs/reference/vertexai/1.68.0/vertexai.language_models.TextEmbeddingModel)\n- [1.67.1](/python/docs/reference/vertexai/1.67.1/vertexai.language_models.TextEmbeddingModel)\n- [1.66.0](/python/docs/reference/vertexai/1.66.0/vertexai.language_models.TextEmbeddingModel)\n- [1.65.0](/python/docs/reference/vertexai/1.65.0/vertexai.language_models.TextEmbeddingModel)\n- [1.63.0](/python/docs/reference/vertexai/1.63.0/vertexai.language_models.TextEmbeddingModel)\n- [1.62.0](/python/docs/reference/vertexai/1.62.0/vertexai.language_models.TextEmbeddingModel)\n- [1.60.0](/python/docs/reference/vertexai/1.60.0/vertexai.language_models.TextEmbeddingModel)\n- [1.59.0](/python/docs/reference/vertexai/1.59.0/vertexai.language_models.TextEmbeddingModel) \n\n TextEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)\n\nCreates a LanguageModel.\n\nThis constructor should not be called directly.\nUse `LanguageModel.from_pretrained(model_name=...)` instead.\n\nMethods\n-------\n\n### batch_predict\n\n batch_predict(\n *,\n dataset: typing.Union[str, typing.List[str]],\n destination_uri_prefix: str,\n model_parameters: typing.Optional[typing.Dict] = None\n ) -\u003e google.cloud.aiplatform.jobs.BatchPredictionJob\n\nStarts a batch prediction job with the model.\n\n### count_tokens\n\n count_tokens(\n prompts: typing.List[str],\n ) -\u003e vertexai.preview.language_models.CountTokensResponse\n\nCounts the tokens and billable characters for a given prompt.\n\nNote: this does not make a prediction request to the model, it only counts the tokens\nin the request.\n\n### deploy_tuned_model\n\n deploy_tuned_model(\n tuned_model_name: str,\n machine_type: typing.Optional[str] = None,\n accelerator: typing.Optional[str] = None,\n accelerator_count: typing.Optional[int] = None,\n ) -\u003e vertexai.language_models._language_models._LanguageModel\n\nLoads the specified tuned language model.\n\n### from_pretrained\n\n from_pretrained(model_name: str) -\u003e vertexai._model_garden._model_garden_models.T\n\nLoads a _ModelGardenModel.\n\n### get_embeddings\n\n get_embeddings(\n texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],\n *,\n auto_truncate: bool = True,\n output_dimensionality: typing.Optional[int] = None\n ) -\u003e typing.List[vertexai.language_models.TextEmbedding]\n\nCalculates embeddings for the given texts.\n\n### get_embeddings_async\n\n get_embeddings_async(\n texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],\n *,\n auto_truncate: bool = True,\n output_dimensionality: typing.Optional[int] = None\n ) -\u003e typing.List[vertexai.language_models.TextEmbedding]\n\nAsynchronously calculates embeddings for the given texts.\n\n### get_tuned_model\n\n get_tuned_model(*args, **kwargs)\n\nLoads the specified tuned language model.\n\n### list_tuned_model_names\n\n list_tuned_model_names() -\u003e typing.Sequence[str]\n\nLists the names of tuned models.\n\n### tune_model\n\n tune_model(\n *,\n training_data: typing.Optional[str] = None,\n corpus_data: typing.Optional[str] = None,\n queries_data: typing.Optional[str] = None,\n test_data: typing.Optional[str] = None,\n validation_data: typing.Optional[str] = None,\n batch_size: typing.Optional[int] = None,\n train_steps: typing.Optional[int] = None,\n tuned_model_location: typing.Optional[str] = None,\n model_display_name: typing.Optional[str] = None,\n task_type: typing.Optional[str] = None,\n machine_type: typing.Optional[str] = None,\n accelerator: typing.Optional[str] = None,\n accelerator_count: typing.Optional[int] = None,\n output_dimensionality: typing.Optional[int] = None,\n learning_rate_multiplier: typing.Optional[float] = None\n ) -\u003e vertexai.language_models._language_models._TextEmbeddingModelTuningJob\n\nTunes a model based on training data.\n\nThis method launches and returns an asynchronous model tuning job.\nUsage: \n\n tuning_job = model.tune_model(...)\n ... do some other work\n tuned_model = tuning_job.deploy_tuned_model() # Blocks until tuning is complete"]]